Subsurface measurement compression and reconstruction

ABSTRACT

An apparatus comprises a subsurface sensor to be positioned in a wellbore formed in a subsurface formation, wherein the subsurface sensor is to detect subsurface measurements. The apparatus includes a processor and a machine-readable medium having program code executable by the processor to cause the processor to generate a combination of functions based on the subsurface measurements, wherein the combination of functions is a subset of functions from a library of functions. The program code is executable by the processor to cause the processor to determine at least one function parameter of at least one function of the combination of functions and determine at least one formation property of the subsurface formation based on the at least one function parameter.

BACKGROUND

The disclosure generally relates to the field of subsurfacecharacterization and more particularly to communication of subsurfacemeasurement series.

Fluid properties and certain other physical properties of matter changedepending on their environment. Certain formation tester tools have theability to measure various physical properties below the surface of theEarth to provide a means of characterizing a formation and determineformation/fluid properties to account for this phenomenon. The formationtester tools can include a device to provide formation pumpoutmeasurements, wherein a sample of formation fluid is isolated from itssurroundings, produced at a certain testing flow rate to the surface,and measured within the borehole at its original environment. Thesein-situ measuring devices make measurements that would be inaccurate ifthe measurements were made at the surface.

Many operations use sophisticated subsurface sensors that generate asignificant amount of data within a short time period. The measurementsare often communicated to the surface using techniques such as fluidpulse telemetry, which limit the data bandwidth to a level far below thedata acquisition rate of these subsurface sensors. Methods that optimizethe data being transmitted to the surface are useful for formationevaluation and/or controlling a drilling operation, stimulationoperation, or well production operation.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure can be better understood by referencingthe accompanying drawings.

FIG. 1 is an elevation view of an onshore drilling system operating adownhole drilling assembly that includes a subsurface sensor system.

FIG. 2 is an elevation view of an onshore wireline system operating awireline tool that includes a subsurface sensor system.

FIG. 3 depicts a flowchart of operations to communicate a subsurfacemeasurement series.

FIG. 4 depicts an example set of functions that can be included in afunction library.

FIG. 5 depicts an example subsurface measurement series and areconstructed measurement series.

FIG. 6 depicts an example computer device.

DESCRIPTION OF EMBODIMENTS

The description that follows includes example systems, methods,techniques, and program flows that embody embodiments of the disclosure.However, it is understood that this disclosure can be practiced withoutthese specific details. For instance, this disclosure refers to usingresults from a genetic algorithm method. Aspects of this disclosure caninstead be applied to other nonlinear optimization methods such as aneural network method or a hybrid optimization method. In otherinstances, well-known instruction instances, protocols, structures andtechniques have not been shown in detail in order to avoid obfuscatingthe description.

Various embodiments relate to a measurement communication system. Asystem can measure a borehole over time using one or more subsurfacesensors inside of a borehole to produce an in-situ subsurfacemeasurement series, wherein a measurement series is a set ofmeasurements over a variable corresponding with the measurements such astime (e.g. a time series) or total volume of sample fluid extracted(e.g. a sample volume series). After obtaining the in-situ measurementseries, a first processor located in the borehole can analyze thesubsurface measurement series by comparing them to one or more sets ofbasis functions from a library of basis functions (“function library”)and generating a combination of a subset of the basis functions from thefunction library (“combination of functions”). The system can use thefirst processor to fit the combination of functions to the measurementseries using fit quality including one or more residual characteristicsthat are based on a difference(s) between the combination of functionsand subsurface measurement series. In some embodiments, the system canaccess multiple function libraries and can select one of the functionlibraries based on the fit quality including residual characteristics.

The system can generate a set of characterizing values of the downholedata (“characterizing set”) based on the combination of functions, theresidual characteristics, and other values. The characterizing set caninclude characterizing values such as a function identifier indicatingeach function of the combination of functions, function parameterscorresponding to each of the functions, and the function weightcorresponding with each of the functions. In addition, thecharacterizing set can include other values such as residualcharacteristics, a paradigm which allows correct interpretation oftransmitted values, etc. Once a transmission threshold is satisfied, thesystem can communicate the characterizing set to a second processor at adifferent location in the borehole away from the subsurface sensor or atthe surface (e.g. a different location at least 20 feet away). Thesystem can use the second processor to generate a reconstructedcombination of functions based on the characterizing set. The system canthen use the reconstructed combination of functions to generate one ormore sets of reconstructed measurement series. The system can analyzethe one or more sets of reconstructed measurement series to determine atleast one of a formation property, fluid property, and well status.

By communicating characterizing values to the surface based on themeasurement series instead of all of the measurement series, the systemcompresses the number of bits needed to communicate the measurementseries. This compression can increase the accuracy and reliability ofmeasurement communication over systems with limited bandwidth. Forexample, a system that is capable of transmitting 8-50 bits per secondcan be used to communicate characterizing values that accurately capturemeasurements collected at a rate of 10 megabytes per second. Inaddition, the characterizing values can be transmitted with multipleredundant signals to increase the reliability of the measurement andreduce the effect of noise on the measurements. Furthermore, the systemcan use the determined properties/statuses to modify a well testingoperation such as reducing a testing flow rate of a sample fluid.Furthermore, the functions selected for fit and their contribution tothe signal reconstruction provides a mechanism for process controlincluding but not limited to drilling, completion and productionoperations and further formation tester sampling operations. For thispurpose the method may be applied to wireline signal representation.Furthermore, the method may be applied to signal monitoring in low poweror low bandwidth scenarios such as but not limited to remote monitoringsuch as remote pipeline monitoring or remote wellhead monitoring orremote subterranean permanent emplacement production monitoring within apetroleum well. These remote monitoring applications would use sensorssimilar to the sensors contained within the formation testing tool butaffixed to the location without the aid of a formation testing tool.Furthermore, the method may be applied to signal processing forretrievable operations such as drill stem testing, well testing, wellintervention such as coiled tubing operations. Furthermore, the methodmay be used for monitoring situations in which telemetry is high speed,but data collection is larger than telemetry rates. Such situationsinclude but are not limited to acoustic monitoring, or fiber opticmonitoring of wells.

Example Well Systems

FIG. 1 is an elevation view of an onshore drilling system operating adownhole drilling assembly that includes a subsurface sensor system. Adrilling system 100 includes a rig 101 located at a formation surface111 and positioned above a borehole 103 within a subterranean formation102. In some embodiments, a drilling assembly 104 can be coupled to therig 101 using a drill string 105. In other embodiments, the drillingassembly 104 can be coupled to the rig 101 using a wireline or aslickline, for example. The drilling assembly 104 can include a bottomhole assembly (BHA). The BHA can include a drill bit 109, a steeringassembly 108, and a logging-while-drilling(LWD)/measurement-while-drilling (MWD) apparatus having a formationtester tool 107. The formation tester tool 107 can include a fluidisolator 117 to isolate a fluid for measurement. The formation testertool 107 can measure various properties (e.g. temperature, pressure,density, composition, contamination, bubble point, compressibility,viscosity, optical properties etc.) of the fluid in the fluid isolator117, either while the fluid is not flowing or while the fluid is flowingat a testing flow rate through the fluid isolator 117. The formationtester tool 107 or another component of the BHA can also include a firstprocessor to perform operations and generate results based on themeasurements made by the formation tester tool 107.

During drilling operations, a mud pump 132 may pump drilling fluid(sometimes referred to as “drilling mud” or simply “mud”) into the drillstring 105 and down to the drill bit 109. The drilling fluid can flowout from the drill bit 109 and be returned to the formation surface 111through an annular area 140 between the drill string 105 and the sidesof the borehole 103. In some embodiments, the drilling fluid can be usedto cool the drill bit 109, as well as to provide lubrication for thedrill bit 109 during drilling operations. Additionally, the drillingfluid may be used to remove subterranean formation 102 cuttings createdby operating the drill bit 109. Measurements or generated results can betransmitted to the formation surface 111 using mud pulses (or otherphysical fluid pulses) traveling through the drilling mud (or otherfluid) in the borehole 103. These mud pulses can be measured at theformation surface 111 and communicated to a second processor in thecontrol and analysis system 110 located at the formation surface 111.

FIG. 2 is an elevation view of an onshore wireline system operating awireline tool that includes a subsurface sensor system. A wirelinesystem 200 includes a rig 201 located at a surface 211 and positionedabove a wellbore 203 within a subterranean formation 202. The wirelinesystem 200 can include a wireline 204 supporting a formation tester tool209 that includes a fluid isolation chamber 219 and a first processor.The fluid isolation chamber 219 can extract and isolate a formationfluid sample from its immediate surroundings. The fluid in the fluidisolation chamber 219 can be tested by the formation tester tool 209 toprovide various formation measurements about the wellbore 203 and thesubterranean formation 202. A control and analysis system 210 located atthe surface 211 can include a second processor and memory device and cancommunicate with elements of the formation tester tool 209.

During well testing operations, the wireline 204 can transmitcharacterizing values generated by the first processor in the formationtester tool 209 to the surface 211 via the wireline 204. In someembodiments, the results provided from the operations disclosed belowcan be transmitted via the wireline 204. Alternatively, the results canbe communicated via fluid pulses traveling through fluids in thewellbore 203 or electromagnetic signals to the surface 211. Once at thesurface 211, the characterizing values can be communicated to the secondprocessor in the control and analysis system 210.

Example Flowcharts

FIG. 3 depicts a flowchart of operations to communicate a subsurfacemeasurement series. FIG. 3 depicts a flowchart 300 of operations thatare described with reference to a system comprising a first processorand a second processor. Operations of the flowchart 300 start at block302.

At block 302, the system obtains a subsurface measurement series. Thesubsurface measurement series can be obtained from a set of subsurfacesensors such as a set of subsurface fluid property sensors. For example,the subsurface measurement series can include fluid pumpout measurementssuch as fluid density measurements, temperature measurements, andpressure measurements. Other example measurements can includemeasurements for a bubble point, compressibility, capacitance,resistivity, viscosity, optical properties, other chemical properties,and other physical properties. In some embodiments, the subsurfacemeasurement series can be a time series of the subsurface measurementseries. For example, fluid pumpout measurements obtained by the systemcan be stored as a time series. As the system obtains a subsurfacemeasurement series, it can update a combination of functions based onthe subsurface measurement.

The system can control various calibration actions or testing operationswhile obtaining the subsurface measurement series to increasemeasurement accuracy and reduce the effect of contaminants such asdrilling mud on the subsurface measurement series. In some embodiments,the system can allow fluid sample flow to occur for a calibration periodor a calibration volume before obtaining the subsurface measurementseries. For example, with reference to FIG. 1, the system can allowisolated fluids to flow through the formation tester tool 107 until acalibration period (e.g. 1-100 hours) is reached or until a calibrationvolume has flowed from the formation through the formation tester tool107 (e.g. 50-1000 liters). In addition, the system can set the testingflow rate of the fluid sample during this calibration period or during ameasurement period to a specific flow rate. As described further belowfor block 354, based on one or more determined formation properties,fluid properties and/or well statuses, the system can control a testingoperation and obtain the subsurface measurement series again afteraltering the testing operation.

At block 304, the system applies transforms to the subsurfacemeasurement series using the first processor. The system can applytransforms to reduce noise, increase accuracy, and increase theefficiency of later operations. In some embodiments, the system canapply transformations such as Fast Fourier transformations, wavelettransformations, and normalization transformations. For example, thesystem can apply a wavelet transformation to the subsurface measurementseries to compress the subsurface measurement series, increasing thecommunication efficiency of later communication operations. Otherexample transforms include principal component analysis, exponentialfitting, etc. Transforms may be applied either to univariate signals ormultivariate signals from one or more sensors.

At block 308, the system determines a selected combination of functionsbased on the subsurface measurement series using the first processor.The system can determine a selected combination of functions byselecting a combination of functions from a set of combinations. Thesystem can select the combination of functions by determining one ormore residual characteristics as described below and then applying a setof criteria based on the one or more residual characteristics.

The system can use various methods to generate a set of combinations andselect a combination of functions from the set of combinations. Thecombination of functions can be a linear combination of basis functionsfrom a function library. As an example, the combination of functions Fcan be represented by Equation 1, wherein each function ƒ_(i) is thei-th function in the combination of functions, N is the total number ofbasis functions in the combination of functions, and w_(i) is acorresponding function weight for η_(i):

$\begin{matrix}{F = {\sum\limits_{i = 1}^{N}\;{w_{i}f_{i}}}} & (1)\end{matrix}$

In Equation 1, a functional form ƒ_(i) may be used more than once. Otherexamples of the combination of functions include multiplicativefunctions (e.g. w₁*ƒ₁*ƒ₂), imbedded functions ƒ₁(ƒ₂), or logicalfunctions (e.g. ƒ₁ from a to b or ƒ₂ from b to c). While the examplesgiven above use two functions to denote the relationships, sets offunctions may be very large (e.g. hundreds or thousands of functions). Aparadigm can be used to improve the accuracy of a representation of asignal by a combination of functions while reducing the overall numberof functions used in the combination of functions. While the system cansystematically generate every possible combination of functions from afunction library and select for a least residual characteristic, thesystem can also use more efficient optimization methods. In someembodiments, the system can determine the combinations of functions of afunction library or even select a function library from a set offunction libraries using machine-learning methods. The system cangenerate combinations of functions or select a library using variousnonlinear optimization methods such as a genetic algorithm (GA) method,a neural network method, or a hybrid algorithm method, wherein thehybrid algorithm can be based on a GA method and artificial neuralnetwork (ANN) method. For example, the system can use a GA method toselect a combination of functions, using one or more of the residualcharacteristics described below as a cost function.

Residual characteristics can be calculated based on a comparison betweenone or more of the subsurface measurement series and one or more valuesfrom a combination of functions. The residual characteristics caninclude values such as a lack of fit (LOF), noise in the dataset, one ormore other model errors (e.g. mean square error, etc.). For example, aresidual characteristic value of mean square error can be determined bytaking a taking the mean of the squares of the difference between thecombination of functions shown in Equation 1 and the subsurfacemeasurement series.

In some embodiments, the system can select which function library to usefrom a set of function libraries. In the case where there is only oneparticular function library available to the system, the system canselect that particular function library by default. Otherwise, thesystem can determine which function library to use based on one or moreresidual characteristics corresponding with that library. For example,the system can select a function library to use from a set of availablefunction libraries by selecting a first function library by default andselecting a second library if a residual characteristic corresponding tothe combination of functions generated from the first library is greaterthan a library threshold (e.g. a LOF library threshold). Alternatively,the system can select a function library to use based on which librarycan be used to generate a combination of functions having a leastresidual characteristic value (e.g. select a combination of functionsand corresponding library based on least LOF).

As another alternative, the system can select a function library basedon pre-determined instructions corresponding with a formation property,fluid property, or well status. For example, the system can determinethat the subsurface measurement series are indicative of a categoricalfluid property of “gas breakout” and, in response, select a firstfunction library from a set of two libraries based on the first functionlibrary being designated for “gas breakout” operations. As anotherexample, the system can determine that the subsurface measurement seriesindicate the categorical fluid property of “multi-phase” and, inresponse, select a first function library from a set of two librariesbased on the first library being designated for “multi-phase” operationsand the second library being designated for “single-phase” operations.

In some embodiments, each function in the function library can bemutually orthogonal. Alternatively, some of functions in the functionlibrary can be non-orthogonal to each other. The function library caninclude various number of basis functions. For example, the functionlibrary can have a total of 100 basis functions, 1000 basis functions,2048 basis functions, 10⁷ basis functions, any number of basis functionsin between the stated number of basis functions, etc. Furthermore,multiple libraries in a set of available function libraries can shareone or more basis functions. Selection of an optimal function librarycan be determined by the cost of transmission bits (higher for largerfunction libraries) and/or the representation capability of the functionset (better accuracy for larger function libraries). A desired bit ratecan be selected with a functional library (or set of libraries) selectedbased on being able to provide a bit rate that is less than or equal tothe desired bit rate, a desired representation accuracy can be selectedwith the desired function library selected to at least meet a desiredaccuracy threshold, or a combination therein. The bit rate can depend onthe size of the function library, the composite number of functionparameters for the functions of the function library, and the number offunctions used to represent a signal. In some embodiments, the number ofbits required to reconstruct a signal increase with increasing size ofthe function parameters used to perform the reconstruction, which candecrease the compression ratio.

In some embodiments, the residual characteristics can includeclassifications of other residual characteristics. For example, thesystem can compare the system noise to a model and classify the systemnoise using various classification methods, such as via a classifiertree. Classifications can indicate that error values are decreasing overtime, increasing over time, heterostatic, homostatic, sinusoidal, etc.Alternatively, or in addition, the classifications of other residualcharacteristics can include an error-tracking function. For example, thesystem can generate an error-tracking function for the LOF in the formof a sinusoidal wave, increasing function, or other function as aclassification of a residual characteristic.

The system can use various criteria when determining the selectedcombination of functions. For example, in addition to machine-learningmethods described above, the system can select a combination offunctions based on the combination of functions having the lowestaverage residual characteristics value amongst all generatedcombinations of functions that includes fewer than a maximum number ofbasis function (e.g. 13 basis functions). A system can select one ormore subset libraries from a function library in order to reduce the bitrate for desired cases. One method for selecting a subset library can beto order the function library into a set of ordered functions. In someembodiments, the ordered functions can be ordered from the mostfrequently used functions to the least frequently used functions.Alternatively, the ordered functions can be ordered by a relatedcriteria of most influential functions to least influential functions.The system can decrease the size of the library to a subset library of adesired size in special circumstances based on a sequential list of theordered functions. Influence can be determined by a functions frequencyof use and/or magnitude of contribution. An example of a specialcircumstance is if the bit rate of the telemetry system decreases, whichcan induce to system to use a smaller library (e.g. by using a subsetlibrary of a function library). Another example of a specialcircumstance can be if sensor parameters or tool parameters areoperating within a specified range. For example, the system can use asubset library A if the pump rate is operating fast, use a subsetlibrary B if a pump rate is operating slowly, use a subset library C ifthe density sensor is vibrating between two predetermined frequencies,or use a subset library D if the density sensor is operating between twodifferent predetermined frequencies. In addition to sensor parametersand tool parameters, environmental conditions such as but not limited totemperature and pressure may also be used to select library subsets. Theresolution of function parameters for functional weights may also beused to adjust the bit rate based on situations including but notlimited to the situational examples given above. Further, the number offunctions used by the system may also be changed based on changes in asystem's situation, including but not limited to the situationalexamples given above. Alternatively, the system can determine a selectedcombination of functions based on which combination of functionsincludes the fewest basis functions amongst the set of combinations thatsatisfies one or more residual characteristic thresholds. For example,the system can select a combination of functions based on which of thecombination of functions includes the fewest basis functions amongst anexample subset, wherein each combination in the example subsetcorrespond with a residual characteristic (e.g. LOF, model error, etc.)that is less than an example maximum residual characteristic threshold(e.g. 5%). In addition, the system can apply additional criteria whendetermining the selected combination of functions. For example, thesystem can determine a first set of combinations using the fewest numberof basis functions that satisfy one or more residual characteristicthresholds and then determine the selected combination of functions byselecting the combination of functions having a least residualcharacteristic value amongst the first set of combinations (e.g. a leastLOF error, a least variance, etc.).

At block 320, the system can generate and/or update a characterizing setfor communication based on the residual characteristics and combinationof functions using the first processor. The system can generate thecharacterizing set if no existing characterizing set is stored in thesystem memory or update the characterizing set otherwise. Thecharacterizing set includes various values usable to generatereconstructed measurement series based on a selected combination offunctions and the selected combination's corresponding residualcharacteristics. In some embodiments, the characterizing set can includea function identifier indicating each of the functions used in thecombination of functions, the corresponding function weights of each ofthe functions, the parameters for each of functions, one or moreresidual characteristics, and/or a paradigm to help accurately interpretthe characterizing set.

For example, the system can generate a characterizing set that includesfunction identifiers “[‘1d5’, ‘4p6’],” their corresponding functionweights “[0.85, 0.15],” and their corresponding parameters “[4.5,39.1152, 3.0, 2.5].” As will be described further below for block 342,the system can use these characterizing values to generate areconstructed combination of functions that includes the basis functionidentified by the identifier “1d5.” If the identifier “1d5” identifies afunction represented by Equation 2 where “m” and “b” are functionparameters, and the identifier “4p6” identifies a function representedby Equation 3 where “c” and “d” are function parameters, thereconstructed combination of functions can be represented in form ofEquation 4, wherein the individual basis functions and theircorresponding parameters are shown in Equations 5 and 6:

$\begin{matrix}{y = {{m \cdot x} + b}} & (2) \\{y = {c^{- x} + d}} & (3) \\{F = {{0.85*{f_{1}(x)}} + {0.15*{f_{2}(x)}}}} & (4) \\{f_{1} = {{4.5 \cdot x} + 39.1152}} & (5) \\{f_{2} = {{3.0 \cdot x} + 2.5}} & (6)\end{matrix}$

Functions may also be continuous or discontinuous vector sets such asbut not limited to Eigen vectors destined to a dataset by methods suchas but not limited to principal component analysis or singular valuedecomposition. As described above, the characterizing set can alsoinclude the residual characteristics. For example, the characterizingset can include one or more LOF values, noise values, and/or model errorvalues, and/or a classifier indicating a status such as “gas breakout”,“asphaltene precipitation”, “emulsion.” In some embodiments, the systemcan transmit a classifier in place of a plurality of quantitativeresidual characteristic values. In some embodiments, the system canincrease the reliability and efficiency of subsurface communication byreducing the number of communicated characterizing values, wherein thereduction occurs from simplifying multiple other residualcharacteristics into a classifier.

In addition to including function parameters and residualcharacteristics into a characterizing set, the system can determine aparadigm based on a selected combination of functions and include theparadigm into the characterizing set. The paradigm can include one ormore paradigm parameters, each of which can be used to help interpretthe characterizing set. For example, the paradigm parameters can includea set of values which represent the number of bits assigned to one ormore values in the characterizing set, a delimiter sequence used toseparate values, a library-indicating identifier, etc.

At block 330, the system determines whether a maximum transmission timethreshold is satisfied. The system can determine that the maximumtransmission time threshold is satisfied if the elapsed time since aprevious transmission of a characterizing set is greater than themaximum time threshold. For example, if the maximum transmission timethreshold is 20 minutes and the elapsed time since a previoustransmission of a downhole is greater than 20 minutes, the system candetermine that the maximum transmission time threshold is satisfied. Ifthe system determines that the maximum transmission time threshold issatisfied, the system can proceed to operations described for block 338.Otherwise, the system can proceed to operations described for block 332.

At block 332, the system determines whether a minimum transmission timethreshold is satisfied. The system can determine that the minimumtransmission time threshold is satisfied if the elapsed time since aprevious transmission of a characterizing set is greater than theminimum time threshold. For example, if the minimum transmission timethreshold is 5 minutes and the elapsed time since a previoustransmission of a downhole is greater than 5 minutes, the system candetermine that the minimum transmission time threshold is satisfied. Ifthe system determines that the minimum transmission time threshold issatisfied, the system can proceed to operations described for block 334.Otherwise, the system can return to operations described for block 302to continue obtaining surface measurements.

At block 334, the system determines if one or more transmission criteriaare satisfied. The one or more transmission criteria can be satisfied invarious cases, such as when a paradigm is changed, when the functions ina selected combination of functions are different from the functions ina previously selected combination of functions, when the LOF increasesabove a fitting threshold, when the model error is greater than modelerror threshold, etc. If the system determines that the one or moretransmission criteria are satisfied, the system can proceed tooperations described for block 338. Otherwise, the system can return tooperations described for block 302 to continue obtaining surfacemeasurements.

At block 338, the system transmits the characterizing set. In someembodiments, the system transmits the characterizing set by sendingsignals via sets of fluid pulses. For example, with reference to FIG. 1,the system can transmit the characterizing set from the formation testertool 107 to the surface via fluid pulse telemetry at a bit rate of 20bits per second (though other bit rates such as 10 bits per second or200 bits per second are possible). Alternatively, the system cancommunicate the characterizing set through a wireline communicationsystem or electromagnetic communication system. During transmission ofthe characterizing set, different values in the characterizing set canbe communicated using different numbers of bits. For example, a libraryidentifier can be communicated using 10 bits, a first function parametercan be communicated using 12 bits, and a second function parameter canbe communicated using 8 bits.

At block 342, the system generates a reconstructed combination offunctions based on the characterizing set. The system can generate thereconstructed combination of functions by selecting basis functionsindicated by the function identifiers of the characterizing set andsubstituting in the corresponding parameters from the transmittedcharacterizing set. For example, with reference to the description forblock 324 above, the system can reconstruct the function shown inEquations 4-6 based on the library-indicating identifier [‘A’],function-indicating identifiers “[‘1d5’, ‘4p6’],” their correspondingfunction weights of 0.85 and 0.15, and their corresponding parameters“[4.5, 39.1152, 3.0, 2.5]”.

At block 346, the system can generate a set of reconstructed measurementseries based on the reconstructed combination of functions and othervalues from the characterizing set. The system can generate the set ofreconstructed measurement series by first determining values using thereconstructed combination of functions and then adding randomization tothe values by using the residual characteristics as parameters in arandom function. In some embodiments, the system can use a Monte Carlomethod to reconstruct the set of random values, wherein the residualcharacteristics described for block 308 can be used to determineparameters used by the Monte Carlo method. For example, the system canuse residual characteristics as Monte Carlo parameters to determineconfidence bands and error tolerances for each of the simulatedmeasurement times in the set of reconstructed measurement series whengenerating the set of reconstructed measurement series. This method canbe particularly useful because of the complicated paradigm nature thatfunctions may be combined.

At block 350, the system can determine a formation property, fluidproperty, and/or well status based on the reconstructed measurementseries. In some embodiments, the system can determine a particularformation or fluid property directly from measurements of thatparticular formation or fluid property. For example, the system candetermine a fluid density over a measured period of time by determiningthe average density of a set of reconstructed measurement series ofdensity. In some embodiments, the system can also determine at least oneof a formation property, fluid property, and well status based on thefunctions of the combination of functions, their corresponding functionweights, and/or classifications transmitted in the characterizing set.

In some embodiments, the system can determine the formation propertyand/or fluid property by applying fitting methods to generate curves forthe reconstructed measurement series. The system can compare thesecurves, values predicted by the curves, integrals of these curves,and/or the derivatives of these curves to a library of known behaviorsto determine the formation property, fluid property, or well status. Inaddition, the system can determine whether one or more physicalphenomena are occurring and associate an indicator of the one or morephysical phenomena. For example, the system can determine that a testingoperation is fractionating reservoir fluid based on a reconstructedmeasurement showing a reduced pressure relative to a total volume offluid collected. Alternatively, or in addition, the system can comparethe curves to a library of known behaviors to determine whether otherphenomenon or compositions are present, such as asphalteneprecipitation, gas drop-out, emulsion, etc. In some embodiments, thesystem can directly analyze the curves to determine formation or fluidproperty. For example, the system can independently determine a dewpoint without referencing a library of known behaviors by determiningwhere generated curves show an intersection at a particular state.

In some embodiments, the system can determine a well status based on thereconstructed measurement series. A well status is a quantitative orcategorical value that reflects at least one parameter of welloperations (e.g. drilling status, drilling speed, production flow rate,whether or not any components are damaged in the well, etc.) The systemcan determine a well status by analyzing the reconstructed measurementseries to generate a status indicator. For example, the system cancompare the curve to a library of known behaviors to determine that mudparticulates are present in a sample of formation fluid. In someembodiments, the system can also compare the curves to a library ofknown behaviors to determine that non-formation effects are interferingwith well testing operations. For example, the system can determine thata reconstructed measurement has a fluid density and electric resistivityassociated with a particular non-formation fluid contaminationpercentage (“contamination level”), and that the particularcontamination level exceeds a contamination threshold. Based on thecontamination level exceeding the contamination threshold, the systemcan generate a status indicator to indicate that non-formation effectsare interfering with well-testing operations. In response, the systemcan generate a status indicator that indicates that a pad in theborehole is leaking. Furthermore, the system can generate various otherstatus operations, each of which can indicate one or multiple wellstatuses.

At block 354, the system can control testing operations or welloperations based on the formation property, fluid property, and/or wellstatus. In some embodiments, controlling testing operations can includesending instructions to a formation tester tool to reduce a testing flowrate, increase a calibration time, change a testing time, modify atesting pressure, and/or change the formation tester tool measurementdepth before testing fluid properties again at the changed measurementdepth. For example, the system can determine that the testing operationis fractionating reservoir fluid or inducing asphaltene precipitationand, in response, reduce a testing flow rate. Alternatively, or inaddition, the system can determine that the contamination level exceedsa contamination threshold and increase a calibration period in response.As another example, the system can determine that a testing flow rate isinsufficient or that the reconstructed measurement series are notindicative of any hydrocarbon presence and, in response, sendinstructions to modify a formation tester tool depth before obtainingmore subsurface measurements. Furthermore, the system can controltesting operations or well operations by providing indicators of theformation property, fluid property, and/or well status to an artificialintelligence control system. For example, the system can provide theindicators of the well status to a feed-forward artificial intelligencesystem to control a drilling direction. In some embodiments, the systemcan determine a formation architecture (which can be a formationproperty) based on which functions are selected from the functionlibrary for signal reconstructions. For example, if similar functionsare selected for pumpout signal reconstruction with similar functionweights and similar function parameters, the system can determine thatthe fluid from those two different zones are in production communicationand are from continuous reservoir sections. Likewise, the system candetermine that regular and monotonically changing function parameters isindicative of fluid compositional grading within a reservoir. Functionparameters representing formation signal reconstruction themselves maybe indicative of formation types or classifications. Function parametersrepresenting fluid signal reconstruction themselves may be indicative offluid types or classifications.

The flowcharts above are provided to aid in understanding theillustrations and are not to be used to limit scope of the claims. Theflowcharts depict example operations that can vary within the scope ofthe claims. Additional operations may be performed; fewer operations maybe performed; the operations may be performed in parallel; and theoperations may be performed in a different order. For example, withrespect to FIG. 3, applying pre-processing as disclosed in block 304 isnot necessary. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented byprogram code. The program code may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable machine or apparatus.

Example Data

FIG. 4 depicts an example set of functions that can be included in afunction library. The plot 400 has a vertical axis 401 and a horizontalaxis 402. The vertical axis 401 corresponds with a normalized signalvalue and the horizontal axis 402 corresponds with a normalized timevalue. The plot 400 includes a first basis function 410, second basisfunction 420, third basis function 430, and fourth basis function 440.Each of the four basis functions can be modified based on one or morefunction parameters. A system can use a function library including thebasis functions 410, 420, 430 and 440 to generate a combination offunctions. In addition, the function library can include various otherfunctions. For example, the function library can include sinusoidalfunctions, directly proportional functions, functions with an offset,exponential functions, exponentially decaying functions, arctanfunctions, etc.

FIG. 5 depicts an example subsurface measurement series and areconstructed measurement series. An idealized density time series plot510 represents an example subsurface measurement series from asubsurface sensor over time. The vertical axis 511 of the idealizeddensity time series plot 510 represents normalized density values. Thehorizontal axis 512 of the idealized density time series plot 510represents times of measurement. With reference to FIG. 3 above, asystem can obtain the measurements represented by the idealized densitytime series plot 510 to generate a combination of functions and acharacterizing set using the operations described for blocks 308 and320.

The system can then communicate the characterizing set based on thesubsurface measurement series and the combination of functions to asecond processor via fluid pulse telemetry. The second processor cangenerate a reconstructed combination of functions based on thecommunicated characterizing set to generate reconstructed measurementseries represented by a reconstructed density time series plot 520. Thevertical axis 521 of a reconstructed density time series plot 520represents simulated normalized density values. The horizontal axis 522of the reconstructed density time series plot 520 represents simulatedtimes of measurement. Using the data of the reconstructed density timeseries plot 520, the system can determine various fluid properties suchas an average fluid density, fluid composition, etc.

Example Computer Device

FIG. 6 depicts an example computer device. A computer device 600includes a processor 601 (possibly including multiple processors,multiple cores, multiple nodes, and/or implementing multi-threading,etc.). The computer device 600 includes a memory 607. The memory 607 canbe system memory (e.g., one or more of cache, SRAM, DRAM, zero capacitorRAM, Twin Transistor RAM, eDRAM, EDO RAM, DDR RAM, EEPROM, NRAM, RRAM,SONOS, PRAM, etc.) or any one or more of the above already describedpossible realizations of machine-readable media. The computer device 600also includes a bus 603 (e.g., PCI, ISA, PCI-Express, HyperTransport®bus, InfiniBand® bus, NuBus, etc.) and a network interface 605 (e.g., aFiber Channel interface, an Ethernet interface, an internet smallcomputer system interface, SONET interface, wireless interface, etc.).

In some embodiments, the computer device 600 includes a characterizingset communication system 611, property determination system 612, andwell system controller 613. The characterizing set communication system611 can perform one or more operations for communicating acharacterizing set, including generating a combination of functions,determining residual characteristics and/or determining paradigmparameters. The property determination system 612 can perform one ormore operations for determining a formation/fluid property or wellstatus, including reconstructing a combination of functions,reconstructing measurements, and/or determining formation/fluidparameters. A well system controller 613 can also perform one or moreoperations for controlling a drilling system, well treatment system, orwireline system. For example, the well system controller 613 can modifythe direction of drill bit, modify the speed of a wireline tool beinglowered into a borehole, or change the pump rate of a fluid into aborehole. Any one of the previously described functionalities can bepartially (or entirely) implemented in hardware and/or on the processor601. For example, the functionality can be implemented with anapplication specific integrated circuit, in logic implemented in theprocessor 601, in a co-processor on a peripheral device or card, etc.Further, realizations can include fewer or additional components notillustrated in FIG. 6 (e.g., video cards, audio cards, additionalnetwork interfaces, peripheral devices, etc.). The processor 601 and thenetwork interface 605 are coupled to the bus 603. Although illustratedas being coupled to the bus 603, the memory 607 can be coupled to theprocessor 601. The computer device 600 can be integrated intocomponent(s) of the drill pipe downhole and/or be a separate device atthe surface that is communicatively coupled to the BHA downhole forcontrolling and processing signals (as described herein). The computerdevice 600 can duplicated and positioned at separate positions aborehole. Alternatively, the computer device 600 can duplicated andpositioned at both a position in a borehole and at the surface of theborehole. In some embodiments, a first computer device similar to thecomputer device 600 can be positioned in the borehole without theproperty determination system 612 and a second computer device similarto the computer device 600 can be positioned in a different locationfrom the first computer device without the characterizing setcommunication system 611.

As will be appreciated, aspects of the disclosure can be embodied as asystem, method or program code/instructions stored in one or moremachine-readable media. Accordingly, aspects can take the form ofhardware, software (including firmware, resident software, micro-code,etc.), or a combination of software and hardware aspects that can allgenerally be referred to herein as a “circuit,” “module” or “system.”The functionality presented as individual modules/units in the exampleillustrations can be organized differently in accordance with any one ofplatform (operating system and/or hardware), application ecosystem,interfaces, programmer preferences, programming language, administratorpreferences, etc.

Any combination of one or more machine-readable medium(s) can beutilized. The machine-readable medium can be a machine-readable signalmedium or a machine-readable storage medium. A machine-readable storagemedium can be, for example, but not limited to, a system, apparatus, ordevice, that employs any one of or combination of electronic, magnetic,optical, electromagnetic, infrared, or semiconductor technology to storeprogram code. More specific examples (a non-exhaustive list) of themachine-readable storage medium would include the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a portable compact disc read-only memory (CD-ROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, amachine-readable storage medium can be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device. A machine-readablestorage medium is not a machine-readable signal medium.

A machine-readable signal medium can include a propagated data signalwith machine readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal can takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Amachine-readable signal medium can be any machine readable medium thatis not a machine-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a machine-readable medium can be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thedisclosure can be written in any combination of one or more programminglanguages, including an object oriented programming language such as theJava® programming language, C++ or the like; a dynamic programminglanguage such as Python; a scripting language such as Perl programminglanguage or PowerShell script language; and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code can execute entirely on astand-alone machine, can execute in a distributed manner across multiplemachines, and can execute on one machine while providing results and oraccepting input on another machine.

The program code/instructions can also be stored in a machine-readablemedium that can direct a machine to function in a particular manner,such that the instructions stored in the machine-readable medium producean article of manufacture including instructions which implement thefunction/act specified in the flowchart and/or block diagram block orblocks.

Use of the phrase “at least one of” preceding a list with theconjunction “and” should not be treated as an exclusive list and shouldnot be construed as a list of categories with one item from eachcategory, unless specifically stated otherwise. A clause that recites“at least one of A, B, and C” can be infringed with only one of thelisted items, multiple of the listed items, and one or more of the itemsin the list and another item not listed. Use of the term “set” can betreated as meaning “group having least one of.” For example, “set ofitems” can be treated as meaning “group of items having at least oneitem.” A formation property is a measurable property of the formationsuch as formation density, amount of hydrocarbons in the formation,formation porosity, formation permeability, etc. A fluid property is ameasurable property of a fluid such as fluid pressure, fluidtemperature, fluid composition, fluid concentration, etc.

EXAMPLE EMBODIMENTS

Example embodiments can include the following:

Embodiment 1: An apparatus comprising: a subsurface sensor for use in aborehole to provide a measurement series; a first processor to receivethe measurement series; and a machine-readable medium having programcode to cause the apparatus to, obtain the measurement series, generatea combination of functions based on the measurement series using thefirst processor, wherein the combination of functions comprises a subsetof functions from a library of basis functions, generate a set ofvalues, wherein the set of values comprises a function identifier andcorresponding function weight for at least one function from thecombination of functions, communicate the set of values to a secondprocessor at a different location from the first processor, the secondprocessor to generate a set of reconstructed measurement series based onthe set of values and determine at least one of a formation property, afluid property, and a well status based on the set of reconstructedmeasurement series.

Embodiment 2: The apparatus of Embodiment 1, wherein the program code tocommunicate the set of values to the second processor comprises programcode to: determine whether the functions in the combination of functionsare different from previous functions in a previous selected combinationof functions; and communicate the set of values based on a determinationthat the functions in the combination of functions are different fromprevious functions in the previous selected combination of functions.

Embodiment 3: The apparatus of any of Embodiments 1-2, wherein theprogram code to communicate the set of values to the second processorcomprises program code to cause the apparatus to transmit the set ofvalues through fluid telemetry, wherein the fluid telemetry comprisestransmission of physical pulses through a fluid in the borehole.

Embodiment 4: The apparatus of any of Embodiments 1-3, wherein a firstvalue of the set of values is communicated using a first number of bits,and wherein a second value of the set of values is communicated using asecond number of bits that is different from the first number of bits.

Embodiment 5: The apparatus of any of Embodiments 1-4, furthercomprising program code executable by the second processor to: generatea reconstructed combination of functions based on the set of values; andgenerate the set of reconstructed measurement series based on thereconstructed combination of functions.

Embodiment 6: The apparatus of any of Embodiments 1-5, furthercomprising program code to reduce a testing flow rate of a sample fluidbeing measured by the subsurface sensor based on at least one of theformation property, the fluid property, and the well status.

Embodiment 7: The apparatus of any of Embodiments 1-6, wherein thecombination of functions is a first combination of functions, andwherein the program code to determine the first combination of functionscomprises program code to: determine a residual characteristic based onthe first combination of functions and the measurement series using adifference between a measurement in the measurement series correspondingwith a time point and a predicted value of the combination of functionsat a same time point; and select the first combination of functions froma set of combinations based on the residual characteristic.

Embodiment 8: The apparatus of any of Embodiments 1-7, wherein thelibrary of basis functions is a first library of basis functions,further comprising program code to select the first library of basisfunctions from a plurality of libraries of basis functions based on theresidual characteristic being less than at least one of a residualcharacteristics threshold and a second residual characteristic, whereinthe second residual characteristic is determined based on a secondcombination of functions generated using a second library of basisfunctions.

Embodiment 9: One or more non-transitory machine-readable storage mediacomprising program code for determining at least one of a formationproperty, a fluid property, and a well status, the program code to:obtain a measurement series using a subsurface sensor in a borehole;generate a combination of functions based on the measurement seriesusing a first processor in the borehole, wherein the combination offunctions comprises a subset of functions from a library of basisfunctions; generate a set of values, wherein the set of values comprisesa function identifier and corresponding function weight for at least onefunction from the combination of functions; communicate the set ofvalues to a second processor at a different location from the firstprocessor; generate a set of reconstructed measurement series based onthe set of values; and determine at least one of the formation property,the fluid property, and the well status based on the set ofreconstructed measurement series.

Embodiment 10: The one or more non-transitory machine-readable storagemedia of Embodiment 9, wherein the program code to communicate the setof values to the second processor comprises program code to: determinewhether the functions in the combination of functions are different fromprevious functions in a previous selected combination of functions; andcommunicate the set of values based on a determination that thefunctions in the combination of functions are different from previousfunctions in the previous selected combination of functions.

Embodiment 11: The one or more non-transitory machine-readable storagemedia of any of Embodiments 9-10, wherein the program code tocommunicate the set of values to the second processor comprises programcode to transmit the set of values through fluid telemetry, wherein thefluid telemetry comprises transmission of physical pulses through afluid in the borehole.

Embodiment 12: The one or more non-transitory machine-readable storagemedia of any of Embodiments 9-11, wherein a first value of the set ofvalues is communicated using a first number of bits, and wherein asecond value of the set of values is communicated using a second numberof bits that is different from the first number of bits.

Embodiment 13: The one or more non-transitory machine-readable storagemedia of any of Embodiments 9-12, further comprising program code toreduce a testing flow rate of a sample fluid being measured by thesubsurface sensor based on at least one of the formation property, thefluid property, and the well status.

Embodiment 14: A method for determining at least one of a formationproperty, a fluid property, and a well status, the method comprising:obtaining a measurement series using a subsurface sensor in a borehole;generating a combination of functions based on the measurement series,wherein the combination of functions comprises a subset of functionsfrom a library of basis functions; generating a set of values, whereinthe set of values comprises a function identifier and correspondingfunction weight for at least one function from the combination offunctions; communicating the set of values to a reconstruction processorat a different location from the subsurface sensor; generating a set ofreconstructed measurement series based on the set of values; anddetermining at least one of the formation property, the fluid property,and the well status based on the set of reconstructed measurementseries.

Embodiment 15: The method of Embodiment 14, wherein communicating theset of values to the reconstruction processor comprises: determiningwhether the functions in the combination of functions are different fromprevious functions in a previous selected combination of functions; andcommunicating the set of values based on a determination that thefunctions in the combination of functions are different from previousfunctions in the previous selected combination of functions.

Embodiment 16: The method of any of Embodiments 14-15, whereincommunicating the set of values to the reconstruction processorcomprises transmitting the set of values through fluid telemetry,wherein the fluid telemetry comprises transmission of physical pulsesthrough a fluid in the borehole.

Embodiment 17: The method of any of Embodiments 14-16, wherein a firstvalue of the set of values is communicated using a first number of bits,and wherein a second value of the set of values is communicated using asecond number of bits that is different from the first number of bits.

Embodiment 18: The method of any of Embodiments 14-17, furthercomprising: generating a reconstructed combination of functions based onthe set of values; and generating the set of reconstructed measurementseries based on the reconstructed combination of functions.

Embodiment 19: The method of any of Embodiments 14-18, furthercomprising reducing a testing flow rate of a sample fluid being measuredby the subsurface sensor based on at least one of the formationproperty, the fluid property, and the well status.

Embodiment 20: The method of any of Embodiments 14-19, wherein thecombination of functions is a first combination of functions, andwherein determining the first combination of functions comprises:determining a residual characteristic based on the first combination offunctions and the measurement series using a difference between ameasurement in the measurement series corresponding with a time pointand a predicted value of the combination of functions at a same timepoint; and selecting the first combination of functions from a set ofcombinations based on the residual characteristic.

1. An apparatus comprising: a subsurface sensor to be positioned in awellbore formed in a subsurface formation, the subsurface sensor todetect subsurface measurements; a processor; and a machine-readablemedium having program code executable by the processor to cause theprocessor to, generate a combination of functions based on thesubsurface measurements, wherein the combination of functions is asubset of functions from a library of functions; determine at least onefunction parameter of at least one function of the combination offunctions; and determine at least one formation property of thesubsurface formation based on the at least one function parameter. 2.The apparatus of claim 1, wherein the program code comprises programcode executable by the processor to cause the processor to determine afunction identifier of the at least one function of the combination offunctions, and wherein the program code executable by the processor tocause the processor to determine the at least one formation propertycomprises program code executable by the processor to cause theprocessor to determine the at least one formation property of thesubsurface formation based on the function identifier.
 3. The apparatusof claim 1, wherein the program code comprises program code executableby the processor to cause the processor to determine a function weightof the at least one function of the combination of functions, andwherein the program code executable by the processor to cause theprocessor to determine the at least one formation property comprisesprogram code executable by the processor to cause the processor todetermine the at least one formation property of the subsurfaceformation based on the function weight.
 4. The apparatus of claim 1,wherein the program code comprises program code executable by theprocessor to cause the processor to determine a magnitude ofcontribution of the at least one function of the combination offunctions, and wherein the program code executable by the processor tocause the processor to determine the at least one formation propertycomprises program code executable by the processor to cause theprocessor to determine the at least one formation property of thesubsurface formation based on the magnitude of contribution.
 5. Theapparatus of claim 1, wherein the program code comprises program codeexecutable by the processor to cause the processor to determine afunction classification of the at least one function of the combinationof functions, and wherein the program code executable by the processorto cause the processor to determine the at least one formation propertycomprises program code executable by the processor to cause theprocessor to determine the at least one formation property of thesubsurface formation based on the function classification.
 6. Theapparatus of claim 1, wherein the program code comprises program codeexecutable by the processor to cause the processor to modify a wellboreoperation of the wellbore based on the at least one formation property.7. The apparatus of claim 6, wherein the wellbore operation comprises atesting operation.
 8. A method comprising: receiving subsurfacemeasurements measured by a subsurface sensor positioned in a wellboreformed in a subsurface formation; generating a combination of functionsbased on the subsurface measurements, wherein the combination offunctions is a subset of functions from a library of functions;determining at least one function parameter of at least one function ofthe combination of functions; and determining at least one of aformation property of the subsurface formation, a fluid property in thewellbore, and a status of the wellbore based on the at least onefunction parameter.
 9. The method of claim 8, further comprising:determining a function identifier of the at least one function of thecombination of functions, wherein determining the at least one of theformation property of the subsurface formation, the fluid property inthe wellbore, and the status of the wellbore comprises determining theat least one of the formation property of the subsurface formation, thefluid property in the wellbore, and the status of the wellbore based onthe function identifier.
 10. The method of claim 8, further comprising:determining a function weight of the at least one function of thecombination of functions, wherein determining the at least one of theformation property of the subsurface formation, the fluid property inthe wellbore, and the status of the wellbore comprises determining theat least one of the formation property of the subsurface formation, thefluid property in the wellbore, and the status of the wellbore based onthe function weight.
 11. The method of claim 8, further comprising:determining a magnitude of contribution of the at least one function ofthe combination of functions, wherein determining the at least one ofthe formation property of the subsurface formation, the fluid propertyin the wellbore, and the status of the wellbore comprises determiningthe at least one of the formation property of the subsurface formation,the fluid property in the wellbore, and the status of the wellbore basedon the magnitude of contribution.
 12. The method of claim 8, furthercomprising: determining a function classification of the at least onefunction of the combination of functions, wherein determining the atleast one of the formation property of the subsurface formation, thefluid property in the wellbore, and the status of the wellbore comprisesdetermining the at least one of the formation property of the subsurfaceformation, the fluid property in the wellbore, and the status of thewellbore based on the function classification.
 13. The method of claim8, further comprising modifying a wellbore operation of the wellborebased on the at least one of the formation property of the subsurfaceformation, the fluid property in the wellbore, and the status of thewellbore.
 14. The method of claim 13, wherein the wellbore operationcomprises a testing operation.
 15. One or more non-transitorymachine-readable media having program code executable by a processor tocause the processor to perform operations comprising: receivingsubsurface measurements measured by a subsurface sensor positioned in awellbore formed in a subsurface formation; generating a combination offunctions based on the subsurface measurements, wherein the combinationof functions is a subset of functions from a library of functions;determining at least one function parameter of at least one function ofthe combination of functions; and determining at least one of aformation property of the subsurface formation, a fluid property in thewellbore, and a status of the wellbore based on the at least onefunction parameter.
 16. The one or more non-transitory machine-readablemedia of claim 15, further comprising: determining a function identifierof the at least one function of the combination of functions, whereindetermining the at least one of the formation property of the subsurfaceformation, the fluid property in the wellbore, and the status of thewellbore comprises determining the at least one of the formationproperty of the subsurface formation, the fluid property in thewellbore, and the status of the wellbore based on the functionidentifier.
 17. The one or more non-transitory machine-readable media ofclaim 15, further comprising: determining a function weight of the atleast one function of the combination of functions, wherein determiningthe at least one of the formation property of the subsurface formation,the fluid property in the wellbore, and the status of the wellborecomprises determining the at least one of the formation property of thesubsurface formation, the fluid property in the wellbore, and the statusof the wellbore based on the function weight.
 18. The one or morenon-transitory machine-readable media of claim 15, further comprising:determining a magnitude of contribution of the at least one function ofthe combination of functions, wherein determining the at least one ofthe formation property of the subsurface formation, the fluid propertyin the wellbore, and the status of the wellbore comprises determiningthe at least one of the formation property of the subsurface formation,the fluid property in the wellbore, and the status of the wellbore basedon the magnitude of contribution.
 19. The one or more non-transitorymachine-readable media of claim 15, further comprising: determining afunction classification of the at least one function of the combinationof functions, wherein determining the at least one of the formationproperty of the subsurface formation, the fluid property in thewellbore, and the status of the wellbore comprises determining the atleast one of the formation property of the subsurface formation, thefluid property in the wellbore, and the status of the wellbore based onthe function classification.
 20. The one or more non-transitorymachine-readable media of claim 15, further comprising modifying awellbore operation of the wellbore based on the at least one of theformation property of the subsurface formation, the fluid property inthe wellbore, and the status of the wellbore.